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Record Nr. |
UNINA9910782270003321 |
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Titolo |
Personalization techniques and recommender systems [[electronic resource] /] / editors, Gulden Uchyigit, Matthew Y. Ma |
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Pubbl/distr/stampa |
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Hackensack, NJ, : World Scientific, c2008 |
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ISBN |
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1-281-94811-X |
9786611948115 |
981-279-702-5 |
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Descrizione fisica |
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1 online resource (334 p.) |
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Collana |
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Series in machine perception and artificial intelligence ; ; v. 70 |
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Altri autori (Persone) |
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MaM. Y (Matthew Y.) |
UchyigitG (Gulden) |
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Disciplina |
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Soggetti |
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Personal communication service systems |
Recommender systems (Information filtering) |
Wireless communication systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Contents; Preface; User Modeling and Profiling; 1. Personalization-Privacy Tradeo s in Adaptive Information Access B. Smyth; 1.1. Introduction; 1.2. Case-Study 1 - Personalized Mobile Portals; 1.2.1. The challenges of mobile information access; 1.2.1.1. Mobile internet devices; 1.2.1.2. Browsing versus search on the mobile internet; 1.2.2. The click-distance problem; 1.2.3. Personalized navigation; 1.2.3.1. Pro ling the user; 1.2.3.2. Personalizing the portal; 1.2.4. Evaluation; 1.2.4.1. Click-distance reduction; 1.2.4.2. Navigation time versus content time |
1.3. Case-Study 2: Personalized Web Search1.3.1. The challenges of web search; 1.3.2. Exploiting repetition and regularity in community- based web search; 1.3.3. A case-based approach to personalizing web search; 1.3.4. Evaluation; 1.3.4.1. Successful sessions; 1.3.4.2. Selection positions; 1.4. Personalization-Privacy: Striking a Balance; 1.5. Conclusions; Acknowledgments; References; BIOGRAPHY; 2. A Deep Evaluation of Two Cognitive User Models for Personalized Search F. Gasparetti and A. Micarelli; 2.1. Introduction; 2.2. Related Work; 2.3. SAM-based User Modeling Approach |
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2.3.1. SAM: search of associative memory2.3.2. The user modeling approach; 2.3.2.1. LTS and STS; 2.3.2.2. Sampling and Recovery; 2.3.2.3. Learning; 2.3.2.4. Interaction with Information Sources; 2.3.3. HAL-based User Modeling Approach; 2.4. Evaluation; 2.4.1. Evaluating User Models in Browsing Activities; 2.4.2. Corpus-based evaluation; 2.4.3. Precision vs. Number of Topics; 2.4.4. Precision vs. Extracted Cues; 2.4.5. Precision vs. Size of STS; 2.4.6. Precision vs. Number of Recovery Attempts; 2.5. Conclusions; References; BIOGRAPHIES |
3. Unobtrusive User Modeling For Adaptive Hypermedia H. J. Holz, K. Hofmann and C. Reed3.1. Introduction; 3.1.1. User modeling in adaptive hypermedia; 3.1.2. Motivation: informal education and the user modeling effect; 3.1.3. Our solution: unobtrusive user modeling; 3.2. Approach; 3.2.1. Classi er-independent feature selection; 3.2.2. Inference design; 3.3. Field Study; 3.3.1. ACUT; 3.3.2. Measurements; 3.3.3. Feature design; 3.3.4. Data collection; 3.3.5. Self-organizing maps; 3.3.6. Revising the features; 3.4. Discussion; Acknowledgments; References; BIOGRAPHIES |
4. User Modelling Sharing for Adaptive e-Learning and Intelligent Help K. Kabassi, M. Virvou and G. A. Tsihrintzis4.1. Introduction; 4.2. Description of Systems of Di erent Domains Sharing a Common User Model; 4.2.1. System for e-Learning in Atheromatosis; 4.2.2. Systems for Intelligent Help in le manipulation and e-mailing; 4.2.3. Error Diagnosis in three systems of different domains; 4.3. Common attributes for evaluating alternative actions; 4.4. Example of a user interacting with three di erent sys- tems; 4.5. User Modelling based on Web Services; 4.5.1. UM-Server's Architecture |
4.5.2. UM-Server's Operation |
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Sommario/riassunto |
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The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed. The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web service |
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